The notion of bisimulation plays a very important role in theoretical computer science where it provides several notions of equivalence between models of computation.
The dynamical equivalence between models of this sort, where populations experience exponential growth asymptotically approaching a threshold, and those models used in neural networks of excitation/inhibition between neurons is well recognised ([ 64] and refs. within).
We establish equivalence between the model and two cognitive biases: risk attitudes are reinforced by experiences (one is more risk averse after disappointment than after elation) and there is a primacy effect (early outcomes have the greatest impact on risk attitude).
García-Cortés and Toro [ 10] have empirically illustrated the validity of their proposal through a numerical example, but they have not presented a formal derivation of the equivalence between their model and the one formalized by Cantet and Fernando [ 2] using the quantitative genetic arguments of Lo et al. [ 1], at least when the goal is to predict breeding values.
Equivalence between the models with size dependent and uniform contact parameters has been checked.
It allows us to check the equivalence between two models in different levels of abstraction, e.g., a Register Transfer Level (RTL) implementation and a non-cycle-accurate specification.
We also analyze the complexity of deciding explanations and partial explanations in the case of succinctly represented context sets, the complexity of deciding explanations in the general case of situations, and the complexity of deciding subsumption and equivalence between causal models.
Finally, on using these matrices and by means of appropriate algebraic operations, we showed the equivalence between both models.
Based on a mathematical equivalence between discrete choice model and Cox proportional hazards model, Cox partial likelihood was used for estimating P values and odds ratios (ORs) [ 7, 8].
After showing the equivalence between the LWR model and a linear optimization problem, several extensions are introduced to model the ramp queues and the capacity drop phenomenon.
